• Oladoyin Akinsuli

The role of both standard AI and generative AI in the sphere of cybersecurity has been considerable; it is possible to describe it as disruptive. This AI, which works with a strictly defined approach and depends on definite rules and algorisms, has been proven to be critical for the purposes of automation of threat identification, acceleration of incidents’ handling, and the ability to predict cyber threats. It has been proven to be effective in addressing all the acknowledged flow threat patterns and performing most of the generic security tasks. The novel generative AI technology implies a new domain of cybersecurity since computer systems can develop unique and individual approaches to complex and constantly changing threats. Generative AI provides the possibility to model the most probable attack schemes, generate realistic data for training, and develop instant response measures. This gets rid of the drawback of classical AI in responding to zero-day attacks and other complex, sustained dangers. This abstract looks at the current roles played by classical and generative AI in modern cybersecurity and the differences as well as similarities between the two. Thus, the publications focus on the key aspects of machine learning and the deep learning AI paradigm equally to build stronger, more flexible, and actively responding security measures for combating threats.

Threat Detection, Anomaly Detection, Data Synthesis, Adversarial Attacks, Automated Responses, Machine Learning, Cyber Defense Strategy

"TRADITIONAL AI VS GENERATIVE AI: THE ROLE IN MODERN CYBER SECURITY", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 7, page no.i431-i448, July-20247, Available :http://www.jetir.org/papers/JETIR2407842.pdf

"TRADITIONAL AI VS GENERATIVE AI: THE ROLE IN MODERN CYBER SECURITY", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 7, page no. ppi431-i448, July-20247, Available at : http://www.jetir.org/papers/JETIR2407842.pdf